Cloud Auto Scaling Using AppDynamics

Are your applications moving to an elastic cloud infrastructure? The question is no longer if, but when – whether that is a public cloud, a private cloud, or a hybrid cloud.

Classic computing capacity models clearly indicate that over-provisioning is essential to keep up with peak loads of traffic while the over-provisioned capacity is largely left under-utilized during non-peak periods. Such over-provisioning and under-utilization can be avoided by moving to an elastic cloud-computing capacity model where just-in-time provisioning and deprovisioning can be achieved by automatically scaling up and down on-demand.

Cloud auto-scaling decisions are often made based on infrastructure metrics such as CPU Utilization. However, in a cloud or virtualized environment, infrastructure metrics may not be reliable enough for making auto-scaling decisions. Auto-scaling decisions based on application metrics, such as request-queue depth or requests per minute, are much more useful since the application is intimately familiar with conditions such as:

When the existing number of compute instances cannot handle the incoming arrival rate of traffic and must elastically scale up additional instances based on a high-watermark threshold on a given application metric

When it’s time to scale back down based on a low-watermark threshold on the same application metric.

Every application service can be expressed as a statistical model of traffic, queues and resources as shown in the diagram below.

For a given arrival rate λ, we need to maximize the service rate μ with an optimum value of n resources. Monitoring either the arrival rate λ itself for synchronous requests or q depth for asynchronous requests will help us tune the application system to see if we need additional service compute instances to meet the demands of the current arrival rate.

Having visibility into this data allows us not only to find bottlenecks in the code but also possibly flaws in design and architecture. AppDynamics provides visibility into these application metrics.

The basic flow for auto-scaling using AppDynamics is shown in the diagram below:

Let’s take an example to illustrate how this actually works in AppDynamics. ACME Corporation has a multi-tier distributed online bookstore application running on AWS EC2:

The front-end E-Commerce tier is experiencing a very heavy volume of requests resulting in the tier going into a Warning (Yellow) state.

Now we will walk through the 6 simple steps that the ACME Corporation will use to exploit the Cloud Auto Scaling features of AppDynamics.

Step 1: Enable display of Cloud Auto Scaling features

To do this, they first select “Setup-> My Preferences” and check the box to “Show Cloud Auto Scaling features” under “Advanced Features”:

Step 2: Define a Compute Cloud and an Image

Then they click on the Cloud Auto Scaling option at the bottom left of the screen:

Next, they click on Compute Clouds and register a new Compute Cloud:

and fill in their AWS EC2 account info and credentials:

Next, they register a new image from which new instances of the E-Commerce tier nodes can be spawned:

and provide the details of that machine image:

By using the Launch Instance button, they can manually test whether it was successfully launched.

Step 3: Define a scale-up and a scale-down workflow

Then, they define a scale-up workflow for the E-Commerce tier with a step to create a new compute instance from the AMI defined earlier:

Next, they define a scale-down workflow for the E-Commerce tier with a step to terminate a running compute instance from the same AMI:

Now, you may be wondering why these workflows are so simplistic and why there are no additional steps to rebalance the load-balancer after every new compute instance gets added or terminated. Well, the magic for that lies in the Ubuntu AMI that bootstraps the Tomcat JVM for the E-Commerce tier. It has the startup logic to automatically join the cluster and also has a shutdown-hook to automatically leave the cluster, by communicating directly with Apache load-balancer mod_proxy.

Step 4: Define an auto-scaling health rule

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Now, they define an auto-scaling health rule for the E-Commerce tier:

and select the E-Commerce Server tier as the scope for the health rule:

and specify a Critical Condition as “Calls per Minute > 3500”, which in this case, represents the arrival rate λ:

and a Warning Condition of “Calls per Minute > 3000”:

Note: It is very important to choose the threshold values for Calls Per Minute in the Critical and Warning conditions very carefully, because failing to do so may result in scaling thrash.

Step 5: Define a scale-up policy

Now, they define a Scale Up Policy which will bind their newly defined Health Rule with a Cloud Auto-scaling action:

Step 6: Define a scale-down policy

Finally, they define another policy that will invoke the Scale-down workflow when the Health rule violation is resolved.

And they’re done!

After a period of time when the Calls per Minute exceeds the configured threshold, they actually witness that the Auto-scaling Health rule was violated, as it shows up under the Events list:

When they drill down into the event, they can see the details of the Health Rule violation:

And when they click on the Actions Executed for the Cloud Auto-Scaling Workflows, they see:

Also, under Workflow executions, they see:

and when they drill-down into it, they see:

Finally, under the Machines item under Cloud Auto Scaling, they can see the actual compute instance that was started as a result of Auto Scaling:

Thus, without any manual intervention, whenever the E-Commerce tier needs additional capacity indicated by the threshold of Calls Per Minute in the Auto-Scaling Health rule, it is automatically provisioned. Also, these additional instances are automatically released when the Calls Per Minute goes below that threshold.

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